Machine Learning
- Experience in data science, applied machine learning, or a related quantitative role, with demonstrated ownership of end-to-end projects.
- Strong grounding in ML, statistics, experimentation and data analysis, including hypothesis testing, causal reasoning, and metric design.
- Experience building and deploying production-grade models or analytical systems in collaboration with engineering teams, including hands-on experience with cloud-based ML infrastructure (e.g., AWS SageMaker, Lambda, S3, ECR) and containerized workflows (Docker, Kubernetes).
- Proven experience designing, analyzing, and interpreting A/B tests in production environments, aligned to business or product goals, including defining success metrics and guarding against common statistical pitfalls.
- Ability to work effectively with cross-functional partners (Product, Engineering, Analytics, Design, Data Engineering), translating between business context and technical solutions.
- Strong problem-framing and prioritization skills, particularly in ambiguous or under-specified problem spaces.
- Proficiency in SQL and Python, with experience using modern data and ML tooling.
- Ownership mindset: proactively identifying problems worth solving, taking accountability for outcomes, and driving initiatives forward independently.
Preferred
- Hands-on experience with search, personalization, recommendations, ranking, or lifecycle modelling.
- Experience with MLOps best practices, including model versioning, CI/CD for ML, monitoring, and operating models in containerized and orchestrated environments (Docker, Kubernetes).
- Experience in e-commerce, marketplace, or subscription-based businesses.
- Familiarity with working in environments with moderate technical debt or evolving data foundations.
- Experience defining and owning metrics, experimentation frameworks, or model performance monitoring in production.
- Demonstrated ability to influence beyond immediate project scope, shaping best practices, standards, or strategy across teams.